2018
Tsinganos, Panagiotis; Skodras, Athanassios
On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection Journal Article
In: Sensors, vol. 18, no. 2, pp. 592, 2018, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: accelerometer, data fusion, fall detection, gyroscope, mHealth, smartphone, wearable sensors
@article{Tsinganos2018b,
title = {On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection},
author = {Panagiotis Tsinganos and Athanassios Skodras},
url = {http://www.mdpi.com/1424-8220/18/2/592},
doi = {10.3390/s18020592},
issn = {1424-8220},
year = {2018},
date = {2018-02-01},
journal = {Sensors},
volume = {18},
number = {2},
pages = {592},
abstract = {In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical attention; thus, their detection is of primary importance. To this effect, many fall detection systems that utilize wearable and ambient sensors have been proposed. In this study, we compare three newly proposed data fusion schemes that have been applied in human activity recognition and fall detection. Furthermore, these algorithms are compared to our recent work regarding fall detection in which only one type of sensor is used. The results show that fusion algorithms differ in their performance, whereas a machine learning strategy should be preferred. In conclusion, the methods presented and the comparison of their performance provide useful insights into the problem of fall detection.},
keywords = {accelerometer, data fusion, fall detection, gyroscope, mHealth, smartphone, wearable sensors},
pubstate = {published},
tppubtype = {article}
}
In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical attention; thus, their detection is of primary importance. To this effect, many fall detection systems that utilize wearable and ambient sensors have been proposed. In this study, we compare three newly proposed data fusion schemes that have been applied in human activity recognition and fall detection. Furthermore, these algorithms are compared to our recent work regarding fall detection in which only one type of sensor is used. The results show that fusion algorithms differ in their performance, whereas a machine learning strategy should be preferred. In conclusion, the methods presented and the comparison of their performance provide useful insights into the problem of fall detection.